There is a question every spine surgeon has asked at some point in their career — usually alone, usually late at night:

"What do we actually know about this?"

Not what a single paper says. Not what a colleague mentioned at a conference two years ago. Not what your personal series of 40 cases suggests.

What do we — the global community of spine specialists — collectively know, right now, backed by real patient data?

Until this week, that question had no answer. Today, it does.

Three Streams of Knowledge. One Query.

Spine surgery generates three distinct streams of intelligence that have always existed in complete isolation.

The literature
Peer-reviewed papers, systematic reviews, meta-analyses — the formal record. Rich, rigorous, and static. Disconnected from daily practice.

Surgeon experience
The accumulated clinical judgment of specialists worldwide — built from cases seen, complications managed, patterns recognized. The most valuable and most invisible intelligence in medicine. On Spinal, verified spine surgeons share complex cases with imaging, clinical scores, and peer discussion. These are now indexed into the knowledge graph — the first structured layer of collective surgical experience in spine medicine.

Patient outcomes
ODI scores, VAS pain ratings, return to work, satisfaction. The ground truth — currently fragmented across EHR systems and isolated registries. SpineBase changes this: surgeons contribute anonymized outcomes following a standardized protocol, earn $SPINE tokens, and build the first decentralized multicenter spine registry.

SpineDAO has now connected all three into a single queryable intelligence layer.

What We Built

The knowledge graph. A Neo4j graph database connecting 57 clinical concepts — procedures, pathologies, complications, outcomes — built with surgical precision, not by engineers guessing at clinical logic.

Ask Vincent. SpineDAO's AI research assistant, available to all verified members on Spinal. A synthesis engine that simultaneously queries PubMed literature, SpineBase registry data, and indexed clinical cases — and returns a structured, attributed answer.

The First Finding

Query: "What are outcomes after TLIF surgery?"

From 245 real patients in a multi-surgeon registry with standardized follow-up at 3, 6, 12, and 24 months:

MIS TLIF (n=40)

• MCID at 12 months (ODI ≥15 pts): 73.9%
• VAS back: 6.9 → 3.9
• VAS leg: 6.4 → 3.8
• Patient satisfaction: 95.5%
• Would repeat surgery: 90.9%
• Early complication rate: 0%

Open TLIF (n=205)

• MCID at 12 months (ODI ≥15 pts): 50.5%
• VAS back: 7.2 → 4.4
• VAS leg: 6.9 → 4.2
• Patient satisfaction: 92.3%
• Would repeat surgery: 91.0%
• Early complication rate: 1.5%

A 23-point gap in clinically meaningful functional improvement between approaches.

SI fusion (125 patients): satisfaction 94.4%, early complication rate 8.0%.

"No randomized controlled trials exist. Existing comparisons are indirect across heterogeneous cohorts. Certainty: MODERATE."

That uncertainty is precisely what TLIF-BAYES was designed to resolve.

The $SPINE Economy

$SPINE is a utility token — not a passive investment vehicle.

• Surgeons earn $SPINE for uploading data, posting cases, and completing expert reviews
• Researchers pay $SPINE to access the registry
• Contributors earn royalties when their data is accessed

56 verified SBT holders · 2,015 expert reviews · 58 clinicians · 8 countries · 255,000 $SPINE distributed

Join Spinal at spinal.science · Upload outcomes at spinebase.app